ASU Regents' Professors Open Access Works
The title “Regents’ Professor” is the highest faculty honor awarded at Arizona State University. It is conferred on ASU faculty who have made pioneering contributions in their areas of expertise, who have achieved a sustained level of distinction, and who enjoy national and international recognition for these accomplishments. This collection contains primarily open access works by ASU Regents' Professors.
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- Creators: Ishchenko, Andrii
- Creators: Ji, Shuiwang
Description
Crystal structure determination of biological macromolecules using the novel technique of serial femtosecond crystallography (SFX) is severely limited by the scarcity of X-ray free-electron laser (XFEL) sources. However, recent and future upgrades render microfocus beamlines at synchrotron-radiation sources suitable for room-temperature serial crystallography data collection also. Owing to the longer exposure times that are needed at synchrotrons, serial data collection is termed serial millisecond crystallography (SMX). As a result, the number of SMX experiments is growing rapidly, with a dozen experiments reported so far. Here, the first high-viscosity injector-based SMX experiments carried out at a US synchrotron source, the Advanced Photon Source (APS), are reported. Microcrystals (5–20 µm) of a wide variety of proteins, including lysozyme, thaumatin, phycocyanin, the human A[subscript 2A] adenosine receptor (A[subscript 2A]AR), the soluble fragment of the membrane lipoprotein Flpp3 and proteinase K, were screened. Crystals suspended in lipidic cubic phase (LCP) or a high-molecular-weight poly(ethylene oxide) (PEO; molecular weight 8 000 000) were delivered to the beam using a high-viscosity injector. In-house data-reduction (hit-finding) software developed at APS as well as the SFX data-reduction and analysis software suites Cheetah and CrystFEL enabled efficient on-site SMX data monitoring, reduction and processing. Complete data sets were collected for A[subscript 2A]AR, phycocyanin, Flpp3, proteinase K and lysozyme, and the structures of A[subscript 2A]AR, phycocyanin, proteinase K and lysozyme were determined at 3.2, 3.1, 2.65 and 2.05 Å resolution, respectively. The data demonstrate the feasibility of serial millisecond crystallography from 5–20 µm crystals using a high-viscosity injector at APS. The resolution of the crystal structures obtained in this study was dictated by the current flux density and crystal size, but upcoming developments in beamline optics and the planned APS-U upgrade will increase the intensity by two orders of magnitude. These developments will enable structure determination from smaller and/or weakly diffracting microcrystals.
ContributorsMartin Garcia, Jose Manuel (Author) / Conrad, Chelsie (Author) / Nelson, Garrett (Author) / Stander, Natasha (Author) / Zatsepin, Nadia (Author) / Zook, James (Author) / Zhu, Lan (Author) / Geiger, James (Author) / Chun, Eugene (Author) / Kissick, David (Author) / Hilgart, Mark C. (Author) / Ogata, Craig (Author) / Ishchenko, Andrii (Author) / Nagaratnam, Nirupa (Author) / Roy Chowdhury, Shatabdi (Author) / Coe, Jesse (Author) / Subramanian, Ganesh (Author) / Schaffer, Alexander (Author) / James, Daniel (Author) / Ketwala, Gihan (Author) / Venugopalan, Nagarajan (Author) / Xu, Shenglan (Author) / Corcoran, Stephen (Author) / Ferguson, Dale (Author) / Weierstall, Uwe (Author) / Spence, John (Author) / Cherezov, Vadim (Author) / Fromme, Petra (Author) / Fischetti, Robert F. (Author) / Liu, Wei (Author) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / Department of Physics (Contributor)
Created2017-05-24
Description
Serial femtosecond X-ray crystallography (SFX) using an X-ray free electron laser (XFEL) is a recent advancement in structural biology for solving crystal structures of challenging membrane proteins, including G-protein coupled receptors (GPCRs), which often only produce microcrystals. An XFEL delivers highly intense X-ray pulses of femtosecond duration short enough to enable the collection of single diffraction images before significant radiation damage to crystals sets in. Here we report the deposition of the XFEL data and provide further details on crystallization, XFEL data collection and analysis, structure determination, and the validation of the structural model. The rhodopsin-arrestin crystal structure solved with SFX represents the first near-atomic resolution structure of a GPCR-arrestin complex, provides structural insights into understanding of arrestin-mediated GPCR signaling, and demonstrates the great potential of this SFX-XFEL technology for accelerating crystal structure determination of challenging proteins and protein complexes.
ContributorsZhou, X. Edward (Author) / Gao, Xiang (Author) / Barty, Anton (Author) / Kang, Yanyong (Author) / He, Yuanzheng (Author) / Liu, Wei (Author) / Ishchenko, Andrii (Author) / White, Thomas A. (Author) / Yefanov, Oleksandr (Author) / Han, Gye Won (Author) / Xu, Qingping (Author) / de Waal, Parker W. (Author) / Suino-Powell, Kelly M. (Author) / Boutet, Sebastien (Author) / Williams, Garth J. (Author) / Wang, Meitian (Author) / Li, Dianfan (Author) / Caffrey, Martin (Author) / Chapman, Henry N. (Author) / Spence, John (Author) / Fromme, Petra (Author) / Weierstall, Uwe (Author) / Stevens, Raymond C. (Author) / Cherezov, Vadim (Author) / Melcher, Karsten (Author) / Xu, H. Eric (Author) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / Department of Physics (Contributor)
Created2016-04-12
Description
Serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs) enables high-resolution protein structure determination using micrometre-sized crystals at room temperature with minimal effects from radiation damage. SFX requires a steady supply of microcrystals intersecting the XFEL beam at random orientations. An LCP–SFX method has recently been introduced in which microcrystals of membrane proteins are grown and delivered for SFX data collection inside a gel-like membrane-mimetic matrix, known as lipidic cubic phase (LCP), using a special LCP microextrusion injector. Here, it is demonstrated that LCP can also be used as a suitable carrier medium for microcrystals of soluble proteins, enabling a dramatic reduction in the amount of crystallized protein required for data collection compared with crystals delivered by liquid injectors. High-quality LCP–SFX data sets were collected for two soluble proteins, lysozyme and phycocyanin, using less than 0.1 mg of each protein.
ContributorsFromme, Raimund (Author) / Ishchenko, Andrii (Author) / Metz, Markus (Author) / Roy Chowdhury, Shatabdi (Author) / Basu, Shibom (Author) / Boutet, Sebastien (Author) / Fromme, Petra (Author) / White, Thomas A. (Author) / Barty, Anton (Author) / Spence, John (Author) / Weierstall, Uwe (Author) / Liu, Wei (Author) / Cherezov, Vadim (Author) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / College of Liberal Arts and Sciences (Contributor) / Department of Physics (Contributor)
Created2015-08-04
Description
Serial femtosecond crystallography (SFX) takes advantage of extremely bright and ultrashort pulses produced by x-ray free-electron lasers (XFELs), allowing for the collection of high-resolution diffraction intensities from micrometer-sized crystals at room temperature with minimal radiation damage, using the principle of “diffraction-before-destruction.” However, de novo structure factor phase determination using XFELs has been difficult so far. We demonstrate the ability to solve the crystallographic phase problem for SFX data collected with an XFEL using the anomalous signal from native sulfur atoms, leading to a bias-free room temperature structure of the human A[subscript 2A] adenosine receptor at 1.9 Å resolution. The advancement was made possible by recent improvements in SFX data analysis and the design of injectors and delivery media for streaming hydrated microcrystals. This general method should accelerate structural studies of novel difficult-to-crystallize macromolecules and their complexes.
ContributorsBatyuk, Alexander (Author) / Galli, Lorenzo (Author) / Ishchenko, Andrii (Author) / Han, Gye Won (Author) / Gati, Cornelius (Author) / Popov, Petr A. (Author) / Lee, Ming-Yue (Author) / Stauch, Benjamin (Author) / White, Thomas A. (Author) / Barty, Anton (Author) / Aquila, Andrew (Author) / Hunter, Mark S. (Author) / Liang, Mengning (Author) / Boutet, Sebastien (Author) / Pu, Mengchen (Author) / Liu, Zhi-jie (Author) / Nelson, Garrett (Author) / James, Daniel (Author) / Li, Chufeng (Author) / Zhao, Yun (Author) / Spence, John (Author) / Liu, Wei (Author) / Fromme, Petra (Author) / Katritch, Vsevolod (Author) / Weierstall, Uwe (Author) / Stevens, Raymond C. (Author) / Cherezov, Vadim (Author) / College of Liberal Arts and Sciences (Contributor) / Department of Physics (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / School of Molecular Sciences (Contributor)
Created2016-09-23
Description
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
ContributorsJi, Shuiwang (Author) / Li, Ying-Xin (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2009-04-21
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Background
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
ContributorsZhang, Wenlu (Author) / Feng, Daming (Author) / Li, Rongjian (Author) / Chernikov, Andrey (Author) / Chrisochoides, Nikos (Author) / Osgood, Christopher (Author) / Konikoff, Charlotte (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ji, Shuiwang (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-12-28